pH driven addressing of silicon nanowires onto Si<sub>3</sub>N<sub>4</sub>/SiO<sub>2</sub>micro-patterned surfaces
Bibliographic record
Abstract
pH was used as the main driving parameter for specifically immobilizing silicon nanowires onto Si3N4 microsquares at the surface of a SiO2 substrate. Different pH values of the coating aqueous solution enabled to experimentally distribute nanowires between silicon nitride and silicon dioxide: at pH 3 nanowires were mainly anchored on Si3N4; they were evenly distributed between SiO2 and Si3N4 at pH 2.8; and they were mainly anchored on SiO2 at pH 2. A theoretical model based on DLVO theory and surface protonation/deprotonation equilibria was used to study how, in adequate pH conditions, Si nanowires could be anchored onto specific regions of a patterned Si3N4/SiO2 surface. Instead of using capillary forces, or hydrophilic/hydrophobic contrast between the two types of materials, the specificity of immobilization could rely on surface electric charge contrasts between Si3N4 and SiO2. This simple and generic method could be used for addressing a large diversity of nano-objects onto patterned substrates.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".